Time Trends in Homicide and Mental Illness in Ontario from 1987 to 2012: Examining the Effects of Mental Health Service Provision
Bibliographic record
Abstract
Objective: We examine the association between rates of homicide resulting in a mental health disposition (termed mentally abnormal homicide [MAH]) and homicides without such a disposition, as well as to province-wide psychiatric hospitalisation and incarceration rates. Method: In this population-based study, we investigate all adult homicide perpetrators ( n = 4402) and victims ( n = 3783) in Ontario from 1987 to 2012. We present annual rates of mentally abnormal and non–mentally abnormal homicide and position them against hospitalisation and incarceration rates. Results: Among the total sample of homicide accused, 3.7% were mentally abnormal. Most (82.5%) had a psychotic disorder at the time of the offense. Contrasted with declining hospitalisation, incarceration, and population homicide rates, the rate of MAH remained constant at an average of .07 perpetrators per 100,000 population. The rate of MAH was not associated with discharges from or average length of stay in psychiatric hospitals (ρ = 0.10; 0.34, P > 0.10), incarceration rates (ρ = 0.16, P = 0.42), or the total homicide rate (ρ = 0.25, P = 0.22). The proportion of MAH perpetrators with a substance use disorder increased modestly over time (β = 0.35, R 2 = 0.12, P = 0.08). Conclusions: The rate of MAH has not changed appreciably over the past 25 years. Declining psychiatric service utilisation was not associated with the rate of homicide committed by people with mental illness and, secondarily, was not linked to increases in the population homicide or incarceration rates. Substance use has become a more prevalent problem for this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".